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Exploring Innovation | ISSN:2319–6378(Online)| Reg. No.:68120/BPL/CE/12 | Published by BEIESP | Impact Factor:4.72
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Volume-1 Issue-1: Published on November 25, 2012
Volume-1 Issue-1: Published on November 25, 2012

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Volume-1 Issue-1, November 2012, ISSN:  2319–6378 (Online)
Published By: Blue Eyes Intelligence Engineering & Sciences Publication Pvt. Ltd. 

Page No.



Kotaprolu Nanda Kishore, Vakkalagadda Prasad, Mada Yaswanth Manikanta, T.Ravi, Anup VSAP Josyula

Paper Title:

Optimised Design Of Dual-Band Cellular Repeater At  Different Frequency Bands (GSM 1800/ DCS, 3G)

Abstract: The intended application of our Cellular Repeater is a system of duplex reception, amplification and transmission used to enhance uplink(UL) and downlink(DL) signals in areas of low signal coverage i.e.; for the situations where signal quality between the base station and the receiver is poor and communication fails. This will be helpful for cellular providers to rectify the problems of poor signal service. This Dual band Cellular Repeater consists of Bidirectional amplifier, receiving and transmitting antennas. This paper discusses our assembling process, beginning with component selection and our difficulty in obtaining the required gain according to the user requirement in the process of testing. This cellular repeater can be operated in 2 different operating frequency bands namely, GSM 1800/DCS and 3G. The operation of the repeater can be switched between the two bands depending on the user requirement using a duplexer which provides proper switching among the bands. This model helps a lot in providing efficient signal service in the weaker coverage areas in the specified band of frequency.

 Bi-Directional Amplifier (BDA), Yagi-Uda antenna, Patch panel antenna, power amplifier, GSM 1800/DCS, 3G


1.      K. V. S. Rao, P. V. Nikitin and S. Lam, “Antenna design for UHF  RFID tags: A review and a practical application,” IEEE Transactions  on Antennas and Propagation, vol. 53, no. 12, pp. 3870-3876, Dec. 2005.
2.     K.P. Ray and Y. Ranga, “CPW-fed modified rectangular printed monopole antenna with slot,” Microwave and Optoelectronics Conference, 2007 IMOC 2007 SBMO/IEEE MTT-S International, pp.79-81, Oct. 29 2007-Nov. 1 2007.

3.    C. Balanis, Antenna Theory, Analysis and Design, 3rd edition, New York: Wiley, 2005.

4.    R. C. Baraniuk, V. Cevher, and M. B. Wakin, “Low-dimensional models for dimensionality reduction and signal recovery, a geometrical perspective,” Proc. IEEE, vol. 98, no. 6, pp. 959–971,Jun. 2010.

5.    O. M. Bucci, G. D’Elia, and M. D. Migliore, “A new strategy to reduce the truncation error in near-field far-field transformations,” Radio Sci.,vol. 35, no. 1, pp. 3–17, Jan.–Feb. 2000.

6.   4S Telecom (P) LTD. BANGALORE  User Manual




Aswathy.Ravikumar, Saritha.R

Paper Title:

An Improved Statistical Model for Protein Secondary Structure Prognostication

Abstract: Genome sequencing projects continue to provide a flood of new protein sequences. Recently there have been advances in protein structure prognostication which in turn has improved fold recognition algorithms. Predicting the secondary structure of proteins is important in biochemistry because the 3D structure can be determined from the local folds that are found in secondary structures. Moreover, knowing the tertiary structure of proteins can assist in determining their functions. The problem of protein secondary structure prognostication with Hidden Markov Models is addressed here. Sequence family information is integrated via the combination of independent predictions of homologous sequences and a weighting scheme.  Hidden Markov models were built for a representative set of just over 1,000 structures from the Protein Data Bank (PDB). The topology of the HMM was restricted to biologically meaningful building blocks.

 HMM, protein structure, Markov Process


1.  Moult J, Hubbard T, Fidelis K, Pedersen J: Critical assessment of methods of protein structure prediction (CASP): Round III.Proteins 1999, 37(suppl 3):2-6.
2.  Zvelebil MJJM, Barton GJ, Taylor WR, Sternberg MJE. Prediction of protein secondary and active sites using the alignment of homologous sequences. J Mol Biol 1987;195:957–961.

3.  G. Pollastri, D. Przybylski, B. Rost, and P. Baldi, “Improving the Prediction of Protein Secondary Structure in Three and Egight Classes Using Recurrent Neural Networks and Profiles,” PROTEINS: Structure, Fuction,and Genetics, vol. 47, pp. 228–235, 2002.

4.  K. Lin, V. A. Simossis, W. R. Taylor, and J. Heringa,“A simple and fast secondary structure prodiction  method using hidden neural networks,” Bioinformatics,vol. 21, no. 2, pp. 152–159, 2005.

5.  J. J. Ward, L. J. McGuffin, B. F. Buxton, and D. T. Jones, “Secondary structure prediction with support vector machines,” Bioinformatics, vol. 19, no. 13, pp.1650–1655, 2003.

6.  J. Guo, H. Chen, Z. Sun, and Y. Lin, “A NovelMethod for Protein Secondary Structure Prediction Using Dual-Layer SVM and Profiles,” PROTEINS: Structure, function, and Bioinformatics, vol. 54, pp.738–743, 2004.

7.  Petrey and B. Honig, “Protein structure prediction: Inroads to biology,”Mol. Cell., vol. 20, pp. 811–819, 2005.

8.  P. Baldi and S. Brunak, Bioinformatics: The Machine Learning Approach,2nd ed. Cambridge, MA: MIT Press, 2001M. Young, The Techincal Writers Handbook.  Mill Valley, CA: University Science, 1989.

9.  Chandonia, J.M. & Karplus, M. (1995). Neural Networks for Secondary Structure and Structural Class Prediction. Protein Sci. 4: pp. 275-285.

10. Durbin, R., Eddy, S., Krogh, A., and Mitchison, G. (1998) Biological sequence analysis: Probabilistic models of proteins and nucleic acids, Cambridge University Press, Cambridge.

11.  Krogh, A., Brown, M., Mian, I. S., Sjölander, K., and Haussler, D. (1994) Hidden markov models in computational biology. Applications to protein modeling. J.Mol.Biol.,235,1501–1531.

12.   Eddy, S. R. (1998) Profile hidden markov models. Bioinformatics,14,755–763